DeepAI AI Chat
Log In Sign Up

Time Series Prediction : Predicting Stock Price

by   Aaron Elliot, et al.
Boston University

Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized linear model requires lesser assumptions but is unable to outperform the martingale. In empirical testing, the RNN model performs the best comparing to other two models, because it will update the input through LSTM instantaneously, but also does not beat the martingale. In addition, we introduce an online to batch algorithm and discrepancy measure to inform readers the newest research in time series predicting method, which doesn't require any stationarity or non mixing assumptions in time series data. Finally, to apply these forecasting to practice, we introduce basic trading strategies that can create Win win and Zero sum situations.


page 1

page 2

page 3

page 4


MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data

Time series forecasting is widely used in business intelligence, e.g., f...

Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models

For a long-time, researchers have been developing a reliable and accurat...

Impact of Data Normalization on Deep Neural Network for Time Series Forecasting

For the last few years it has been observed that the Deep Neural Network...

Hybrid symbiotic organisms search feedforward neural net-works model for stock price prediction

The prediction of stock prices is an important task in economics, invest...

Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting

Accurate travel products price forecasting is a highly desired feature t...

Visualising Deep Network's Time-Series Representations

Despite the popularisation of the machine learning models, more often th...